Abstract: In Unani medicine, Bawl (urine) is recognized as a key diagnostic tool, with humoural imbalances assessed via parameters like color, consistency, sediment, clarity, froth, odor, and volume. This conceptual review explores how these classical diagnostic indicators may be contextualized alongside modern urinalysis markers (e.g., bilirubin, protein, ketones, and sedimentation) and examined through emerging artificial intelligence (AI) frameworks. Potential applications include ResNet-18 for color classification, You Only Look Once version 8 (YOLOv8) for sediment detection, long short-term memory (LSTM) for viscosity estimation, and EfficientDet for froth analysis, with standardized urine images/videos forming the basis of future datasets. Additionally, a comparative ontology is proposed to align Unani perspectives with diagnostic approaches in traditional Chinese medicine, encouraging cross-system integration. By synthesizing classical epistemology with computational intelligence, this review highlights pathways for developing AI-based decision support systems to promote personalized, accessible, and telemedicine-enabled healthcare.